Inequality: first-world problems

Intro: I don’t think the social science in The Spirit Level is very good. I suspect I can do better, but that first requires thinking about what I want to do.

Does the prevalence of first-world problems in the first-world really require a complicated explanation? Now that there’s no shortage of food for most, is it surprising that many eat and drink more than is necessary? Is the amount of dysfunction we see in western countries just a reflection of human imperfection? Well, we know that any society is imperfect and can be improved. More relevant questions are whether the potential for improvement is large enough to justify transformation, and what sort of transformations might actually bring about such improvement.

Centuries of revolutions with decidedly mixed results might lead us to try to answer these questions based on empirics. This is hard: no experiment with a control even close to perfect is possible. We rely on observational data, which leads to the usual problems of causal inference. It gets worse: there’s very little longitudinal data, which leaves us with cross-sections. And individual-level survey data that’s comparable across countries rarely exists, which means we have ecological inference problems on top of that.

Look at a graph of life expectancy against income:

The graph would be more informative with GDP on a log scale. But let’s grant the point that returns to national life expectancy diminish as per capita GDP rises. This is interesting! But it’s not nearly enough to inform solid inferences. It tells us very little about what’s going on at the individual level. We may know that within countries, life expectancy is correlated with socio-economic status. A much more interesting approach would be to compare people at the same level of income across countries. But this is much harder.
The same goes for any graph of group averages against group averages. Without some kind of individual-level data or at least stratification, it’s very difficult to get real insight into the relationship between two variables — let alone start the process of trying to control for all the other factors that confound the relationship.